In recent years, various attempts have been made to realize automatic driving of automobiles. For the realization of the automatic driving of automobiles, it is important to correctly detect objects around the vehicle such as vehicles, pedestrians, and obstacles, and to avoid dangers based on the result of detection while the vehicle is running. As a technique for detecting surrounding objects with high accuracy, object-detecting techniques utilizing various types of sensors and radars are known.
On the other hand, as a technique for avoiding dangers during driving, techniques for a system of plural objects and an own vehicle are known, which use information about position and speed of the own vehicle and information about positions and speed of plural objects other than the own vehicle to generate expected paths of objects including the own vehicle, and predict a possibility of collision of two objects among the objects constituting the system (see, for example, Nonpatent Literature 1). This technique predicts possible paths of all the objects constituting the system according to operation series of same framework based on a concept of probability, and outputs the predicted paths. Thereafter, a path, according to which the safest situation is realized for the entire system including the own vehicle, is found and output based on the predicted results.    Nonpatent Literature 1: Broadhurst, S. Baker, and T. Kanade, “Monte Carlo Road Safety Reasoning”, IEEE Intelligent Vehicle Symposium (IV2005), IEEE, (2005 June)